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Optimization of Image Library Visualization and Layout for Enhanced User Interaction

This paper presents a novel approach to optimizing the visualization and layout of image libraries using the Personal Digital Historian (PDH) system. It explores context and user modeling to enhance image retrieval through content-based methods while minimizing visual clutter. Key techniques include polar coordinate layouts, PCA for dimensionality reduction, and cost functions to reduce overlap and deviation of displayed images. User-centered design principles are vital, incorporating user preferences and semantic understanding. Future work aims to refine automated layouts and improve image summarization.

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Optimization of Image Library Visualization and Layout for Enhanced User Interaction

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  1. Visualization & Layout for Image Libraries Baback Moghaddam, Qi Tian IEEE Int’l Conf. on CVPR 2001 Speaker:蘇琬婷 NCKU CSIE

  2. Outline • System Introduction • Visualization and Layout Optimization • Context and User Modeling • Discussion NCKU CSIE

  3. System Introduction-PDH • Personal Digital Historian (PDH) • Interface Design : • Polar coordinate visual layout • circular display area • touch sensitive table surface • top projection table with a whiteboard as the table surface NCKU CSIE

  4. PDH Table NCKU CSIE

  5. 4W’s: The Organization Principal What Where When Who NCKU CSIE

  6. NCKU CSIE

  7. Content-based Visualization • Contend-based Image Retrieval(CBIR) • Images would be indexed by their visual contents • Feature(content) extraction • Visualization • Traditional interfaces • PCA Splats • Display optimization NCKU CSIE

  8. Traditional Systems • Visualization • Simple 1-D list • Sorted by decreasing similarity to the query • Drawback • Relevant images can appear at separate and distant locations in the list • Improvement • 2-D display technique NCKU CSIE

  9. Top 20 Retrieved Images • Ranked top to bottom and left to right NCKU CSIE

  10. PCA Splats • Principal component analysis(PCA) • project the images from the high-dimensional feature space to the 2-D screen • 37 visual features(color, texture, structure) • on the basis of the first two principal components normalized by the respective eigenvalues • The maximum distance preservation from the original high-dimensional feature space to 2-D space NCKU CSIE

  11. Display Optimization • The drawback of PCA splat • images are partially or totally overlapped • Optimization • Minimizing overlap (decreasing the overlap of the images) • Minimizing deviation (deviating as little as possible from their initial PCA splat positions) • Minimizing the total cost NCKU CSIE

  12. Cost Function F(p) : cost function of the overall overlap G(p) :cost function of the overall deviation from the initial image positions S : scaling factor and S = (N-1)/2 N : the number of images λ: weight and λ≧ 0 NCKU CSIE

  13. i j Minimizing Overlap ri : image size is represented by its radius ,i = 1,…,N (xi, yi) : image center coordinates u : measure of overlapping σf : curvature-controlling factor range of F(p): (N-1)+(N-2)+…+1 = N(N-1)/2 NCKU CSIE

  14. Minimizing Deviation ：the optimized and initial center coordinates of the ith image, respectively v : measure of deviation σg : curvature-controlling factor range of G(p) : N range of F(p) : N(N-1)/2 ∴S = (N-1)/2 NCKU CSIE

  15. Optimized PCA Splat NCKU CSIE

  16. Context and User Modeling • Image content and “meaning” is ultimately based on semantics • user’s notion of content : high-level concept • visual features : low-level concept NCKU CSIE

  17. NCKU CSIE

  18. Context and User Modeling • User modeling or “context awareness” • constantly be aware of and adapting to the changing concepts and preferences of the users • learn from a user-generated layout • a novel feature weight estimation scheme : α-estimation • α: weighting vector for feature (color, texture, structure) • α = (αc, αt, αs)T • αc,t,s : the weight for color, texture, structure • αc + αt + αs = 1 NCKU CSIE

  19. Estimation of Feature Weights Xc, t, s : Lc, t, s × Nmatrix where the ith column is the color, texture, structure feature vector of the ith image, i = 1,…,N Lc, t, s : the lengths of color, texture, structure features dij : the distance Euclidean-based between the ith image and the jth image • minimizing with an Lp norm (with p = 2) • non-negative least squares solutions NCKU CSIE

  20. an example of a user-guided layout αc = 0.3792 αt = 0.5269 αs = 0.1002 NCKU CSIE

  21. PCA splat on larger set of images estimated weight randomly generated weight NCKU CSIE

  22. user-guided layout computer layout User Modeling for Automatic Layout NCKU CSIE

  23. Future Work • Having the system learn the feature weights from various sample layouts provided by the user • Incorporate visual features with semantic labels for both retrieval and layout • Incorporation of relevance feedback • Automatic “summarization” and display of large image collections NCKU CSIE

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